add extraction script

extract_data.py

@@ -0,0 +1,91 @@
+"""Script to generate splits for benchmarking text embedding clustering.
+Data and preprocessing based on 10kGNAD dataset (https://github.com/tblock/10kGNAD)."""
+
+import random
+import re
+import sqlite3
+import sys
+
+import jsonlines
+import numpy as np
+import pandas as pd
+from bs4 import BeautifulSoup
+from sklearn.model_selection import train_test_split
+from tqdm import tqdm
+
+random.seed(42)
+
+# path to corpus file, can be retrieved from here: https://github.com/tblock/10kGNAD/releases/download/v1.0/corpus.sqlite3
+DATA_PATH = sys.argv[1]
+
+INCLUDE_BODY = (
+    False  # True: combine title and article body (p2p), False: only title (s2s)
+)
+ARTICLE_QUERY = f"SELECT Path, Title{', Body' if INCLUDE_BODY else ''} FROM Articles WHERE PATH LIKE 'Newsroom/%' AND PATH NOT LIKE 'Newsroom/User%' ORDER BY Path"
+
+NUM_SPLITS = 10
+SPLIT_RANGE = np.array([0.1, 1.0])
+
+
+def get_split(frame, split_range=SPLIT_RANGE):
+    samples = random.randint(*(split_range * len(frame)).astype(int))
+    return frame.sample(samples).to_dict("list")
+
+
+def write_sets(name, sets):
+    with jsonlines.open(name, "w") as f_out:
+        f_out.write_all(sets)
+
+
+conn = sqlite3.connect(DATA_PATH)
+cursor = conn.cursor()
+
+samples = []
+for row in tqdm(cursor.execute(ARTICLE_QUERY).fetchall(), unit_scale=True):
+    path, title = row[0], row[1]
+
+    text = title
+
+    if INCLUDE_BODY:
+        body = row[-1]
+        soup = BeautifulSoup(body, "html.parser")
+
+        # get description from subheadline
+        description_obj = soup.find("h2", {"itemprop": "description"})
+        if description_obj is not None:
+            text += (
+                " " + description_obj.text.replace("\n", " ").replace("\t", " ").strip()
+            )
+
+        # get text from paragraphs
+        text_container = soup.find("div", {"class": "copytext"})
+        if text_container is not None:
+            for p in text_container.findAll("p"):
+                text += " " + (
+                    p.text.replace("\n", " ")
+                    .replace("\t", " ")
+                    .replace('"', "")
+                    .replace("'", "")
+                    + " "
+                )
+        text = text.strip()
+
+    # remove article autors
+    for author in re.findall(
+        r"\.\ \(.+,.+2[0-9]+\)", text[-50:]
+    ):  # some articles have a year of 21015..
+        text = text.replace(author, ".")
+
+    # get label from path
+    label = path.split("/")[1]
+    samples.append([text, label])
+
+conn.close()
+
+samples = pd.DataFrame(samples, columns=["sentences", "labels"])
+
+sets = []
+for _ in range(NUM_SPLITS):
+    sets.append(get_split(samples))
+
+write_sets("test.jsonl", sets)